Detecting
Dynamic Patters
with a
Historical Trace Representation
and
Sound Representation(not working yet)
When Tributaries are long
Now that you're familiar with the basic idea ideas of N, K Boolean systems, let's examine their representation with Smilie 3 for a more complex example with N=100 nodes.
FIRST, click the DELAY button and SET delay to around 100 msec., then click the PLAY button.
The historical trace will move across the veiwing frame in a pattern that reflects the shifts inherent to the basin the system is in. This system has very long tributaries so it have to run a few hundred iterations in order to fall into cyclic attractor patterns. Smilie 3 (historical trace) makes detecting the shift from tributary to attractor much easier than twinkling nodes do.
The fundamental epistemological questions posed by a dynamic ecology to all sentient beings include noticing which basin a system is in and noticing when the dynamics shift from one basin to another. Which repetitive pattern of behaviro characterizes the prey in at this moment? A predator needs to be able to extract basin patterns from the enviroment and adjust its behavior to the differences in those patterns.
Perturbing the System. The Perturb button allows you to pseudo-randomly change the state of some percentage (chosen by you but defaulting to 50%) of the system's nodes. If you perturb the system does it change to another basin? Which? As the system is perturbed can you learn to recognize its many basins? Can you tell one from another?
This is a rather complex system with many basins (86 basins were identified in one search). The basin lengths include L=4, 8, 12, 16, 32, 40, and 72 and the tributaries are often a few hundred iterations long. Learning to recognize these basins with Smilie 3 as you perturb the system mulitple times is much easier with Smilie 3 than it is with the Twinkling Nodes visual representation.